
How to integrate loop closure detection and global pose optimization into the random optimization framework? It is interesting to study random optimization based pose graph optimization or bundle adjustment when large transformations are involved.

How to unify traditional approach and random optimization into a single framework? When camera motion slows down, it is of course a good option to enhance camera tracking with photometric features. Here, how to bridge and switch smoothly between the two technique is worth of investigating.

How to fuse multi-modal input to further improve the robustness of fast-motion camera tracking?

How to realize autonomous reconstruction with mobile robots or drones with our technique? There are several technical issues to address, e.g., how to integrate our method with online motion planning [1]}, [2]}, [3]}, [4]}.

It is interesting to extend our random optimization to distributed bundle adjustment [5]}, [6]} for collaborative reconstruction by a network of robots or drones. Here, the small overlap between distributed nodes also calls for a robust pose optimization.

Another promising and interesting direction is to explore the combination of random optimization and deep priors for online and/or semantic scene reconstruction [7]}, [8]}, [9]}.

